Classification of B-Cell Acute Lymphoblastic Leukemia Microscopic Images Using Crow Search Algorithm

The objective of this research work is to use Crow Search Algorithm for classification of blood smear microscopic images into two classes: Leukemic B-lymphoblast cells (cancer cells) and normal B-lymphoid precursors (normal cells). Crow Search Algorithm is generally used to crack numerical optimization, training neural networks and feature selection problems. This research work uses Crow Search algorithm as a transformation technique to convert non-linearly separable data points as linearly separable data points. Microscopic image dataset named C-NMC is collected from cancer imaging archives website and the microscopic images of 30 cancer subjects and 30 normal subjects are considered in this analysis. To prove the significant performance of crow search algorithm as transformation technique based classifier, other popular unsupervised classification techniques like K-Means and Fuzzy C Means are used. Remarkably 87% of accuracy is achieved when crow search algorithm is used as classifier.